Cook Darren, Peters Dorian, Moradbakhti Laura, Su Ting, Da Re Marco, Schuller Bjorn W, Quint Jennifer, Wong Ernie, Calvo Rafael A
Dyson School of Design Engineering, Imperial College London, London, UK.
Imperial College Healthcare NHS Trust, London, UK.
Digit Health. 2024 Jun 17;10:20552076241258276. doi: 10.1177/20552076241258276. eCollection 2024 Jan-Dec.
Millions of people in the UK have asthma, yet 70% do not access basic care, leading to the largest number of asthma-related deaths in Europe. Chatbots may extend the reach of asthma support and provide a bridge to traditional healthcare. This study evaluates 'Brisa', a chatbot designed to improve asthma patients' self-assessment and self-management.
We recruited 150 adults with an asthma diagnosis to test our chatbot. Participants were recruited over three waves through social media and a research recruitment platform. Eligible participants had access to 'Brisa' via a WhatsApp or website version for 28 days and completed entry and exit questionnaires to evaluate user experience and asthma control. Weekly symptom tracking, user interaction metrics, satisfaction measures, and qualitative feedback were utilised to evaluate the chatbot's usability and potential effectiveness, focusing on changes in asthma control and self-reported behavioural improvements.
74% of participants engaged with 'Brisa' at least once. High task completion rates were observed: asthma attack risk assessment (86%), voice recording submission (83%) and asthma control tracking (95.5%). Post use, an 8% improvement in asthma control was reported. User satisfaction surveys indicated positive feedback on helpfulness (80%), privacy (87%), trustworthiness (80%) and functionality (84%) but highlighted a need for improved conversational depth and personalisation.
The study indicates that chatbots are effective for asthma support, demonstrated by the high usage of features like risk assessment and control tracking, as well as a statistically significant improvement in asthma control. However, lower satisfaction in conversational flexibility highlights rising expectations for chatbot fluency, influenced by advanced models like ChatGPT. Future health-focused chatbots must balance conversational capability with accuracy and safety to maintain engagement and effectiveness.
英国数百万民众患有哮喘,但70%的患者无法获得基本护理,这导致英国成为欧洲哮喘相关死亡人数最多的国家。聊天机器人或许能够扩大哮喘支持服务的覆盖范围,并为传统医疗保健搭建桥梁。本研究对一款名为“布里萨”的聊天机器人进行评估,该机器人旨在改善哮喘患者的自我评估和自我管理能力。
我们招募了150名被诊断患有哮喘的成年人来测试我们的聊天机器人。参与者通过社交媒体和一个研究招募平台分三批招募。符合条件的参与者可通过WhatsApp或网络版使用“布里萨”28天,并完成入组和退出调查问卷,以评估用户体验和哮喘控制情况。利用每周症状跟踪、用户互动指标、满意度测量和定性反馈来评估聊天机器人的可用性和潜在效果,重点关注哮喘控制的变化和自我报告的行为改善情况。
74%的参与者至少与“布里萨”互动过一次。观察到较高的任务完成率:哮喘发作风险评估(86%)、语音记录提交(83%)和哮喘控制跟踪(95.5%)。使用后,报告显示哮喘控制情况有8%的改善。用户满意度调查表明,在帮助性(80%)、隐私(87%)、可信度(80%)和功能(84%)方面得到了积极反馈,但也指出需要提高对话深度和个性化程度。
该研究表明,聊天机器人在哮喘支持方面是有效的,风险评估和控制跟踪等功能的高使用率以及哮喘控制方面具有统计学意义的改善都证明了这一点。然而,对话灵活性方面较低的满意度凸显了受ChatGPT等先进模型影响,人们对聊天机器人流畅性的期望不断提高。未来以健康为重点的聊天机器人必须在对话能力与准确性和安全性之间取得平衡,以保持参与度和有效性。